The present invention is concerned with adaptive equalization of channels for data transmission or storage, and in particular with a method of adapting a distributed-arithmetic equalizer by updating a look-up table containing values that determine the equalizer characteristics, and further with look-up table updating means in an adaptive distributed-arithmetic equalizer apparatus for channel equalization in a partial-response communication or storage system.
A self-training adaptive equalization scheme is known from the following publication: Y. Sato, "A Method of Self-Recovering Equalization for Multi-Level Amplitude Modulation Systems", IEEE Trans. Comm., Vol. COM-23, pp. 679-682, June 1975. In the scheme disclosed, updating of the equalizer coefficients requires N multiplications as well as the computation of a particular function g.sub.k ; the latter requires, in the case of Partial-Response Class-IV (PRIV) signaling, an inversion of the PRIV channel and generation of a two-level PRIV signal. Furthermore, in the steady state the described linear adaptive equalizer undergoes enhanced tap noise with respect to a decision-directed adaptive equalizer.
A new filter design was disclosed in an article by A. Peled et al. "A New Hardware Realization of Digital Filters", IEEE Trans. Acoustics, Speech, Signal Processing, Vol. ASSP-22, pp. 456-462, December 1974. The new technique has become known as "distributed-arithmetic" filter design. However, the publication does not disclose any application of the filter as an adaptive equalizer.
An adaptive equalizer using a distributed-arithmetic architecture was described in the following publication: C. F. N. Cowan et al., "A Digital Adaptive Filter Using a Memory-Accumulator Architecture: Theory and Realization", IEEE Trans. Acoustics, Speech, Signal Processing, Vol. ASSP-31, pp. 541-549, June 1983. The look-up table value updating scheme disclosed in this article is based on a least mean-square algorithm, and no attempt is made to use the distributed-arithmetic equalizer in a self-training mode. For training the equalizer, transmission and recognition of a training sequence would normally be required.
It would be desirable to have an equalization method and apparatus avoiding the disadvantages of known equalization devices and procedures. Therefore it is an object of the invention to provide an equalization scheme using a distributed-arithmetic technique, which is self-training on the received data signal and therefore needs no training sequence.
It is another object to devise a self-training adaptive equalization scheme with low tap noise in the steady state.
It is a further object to provide a self-training adaptive equalization scheme which allows a reduction in hardware complexity for the equalizer apparatus.